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Preparation of Monodomain Liquid Crystal Elastomers and Liquid Crystal Elastomer Nanocomposites
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Published on: February 6, 2016

Neural network-based tensor models for liquid crystals with molecular-level information.

Baoming Shi1, Apala Majumdar2, Lei Zhang3

  • 1Peking University, School of Mathematical Sciences, Beijing 100871, China.

Physical Review. E
|February 20, 2026
PubMed
Summary
This summary is machine-generated.

A new neural network tensor (NN-tensor) model for liquid crystals (LCs) offers superior accuracy and phase transition prediction compared to traditional models. This efficient framework accurately computes complex LC microstructures across multiple phases.

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Area of Science:

  • Materials Science
  • Computational Physics
  • Soft Matter Physics

Background:

  • The Landau-de Gennes (LdG) model is a continuum theory for liquid crystal (LC) phases.
  • LdG models are less accurate and physically informed than molecular-level simulations.
  • Accurate modeling of LC phases is crucial for materials science and device applications.

Purpose of the Study:

  • To develop a novel neural network-based tensor (NN-tensor) model for liquid crystals.
  • To improve the accuracy and physical fidelity of LC phase modeling.
  • To efficiently compute stable LC configurations and resolve complex microstructures.

Main Methods:

  • Developed an NN-tensor model supervised by an underlying molecular model.
  • Integrated the NN-tensor model within a second neural network for efficient computation.
  • Validated the model for nematic and smectic liquid crystal phases.

Main Results:

  • The NN-tensor model achieved energy precision comparable to molecular models.
  • It accurately captured the isotropic-nematic phase transition, surpassing the LdG model.
  • The model quantitatively predicted smectic layer thickness and resolved complex microstructures like Omega and T-shaped grain boundaries.

Conclusions:

  • The NN-tensor framework provides a unified, efficient, and physically faithful approach for computing LC configurations.
  • This method overcomes limitations of conventional approaches in resolving intricate LC microstructures.
  • The NN-tensor model represents a significant advancement in simulating diverse liquid crystal phases.